Aerial image classification using texture and color-based descriptors
Daniel Cortés, Gustavo Calderón, Antonio Arista, Karina Toscano, Mariko Nakano-Miyatake · 2016
In this paper, we evaluate eleven image descriptors for urban-rural classification using aerial images. The eleven image descriptors are composed by seven texture-based descriptors and four combinations of texture and color descriptors. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From the evaluation, the combination of Gabor descriptor and Dominant Color descriptor provides a better performance, obtaining accuracy more than 91%.